Top AI Jobs in Los Angeles, CA

The role involves leading the implementation of AI solutions for productivity, integrating technology with business functions, and managing complex workflows while staying updated on AI advancements relevant to media.

Develop and operate production ML and AI systems powering patient experiences such as matching, ranking, recommendations, onboarding, and personalization. Build reusable ML infrastructure, pipelines, evaluation, observability, and model-serving capabilities. Provide technical leadership across the ML lifecycle, guide architecture, evaluate custom versus foundation-model approaches, and partner with ML and Patient Engineering teams to deliver reliable, scalable AI products.

Design and implement AI agents, plugins, automations, and enterprise integrations for HR Technology platforms. Lead requirements gathering, technical analysis, solution design, testing, documentation, deployment, security, and data integrity efforts across HR, IT, and business stakeholders. Integrate platforms including ServiceNow, Workday, and other enterprise applications while supporting the HR automation and AI roadmap.

Build and manage AI-powered workflows, agents, automations, and internal applications for Deal Desk and GTM teams. Identify automation opportunities, develop prototypes, scale solutions to production, integrate enterprise data and APIs, implement human validation and audit controls, optimize LLM costs, document tools, measure adoption, and train users. The role requires Deal Desk or RevOps experience plus practical knowledge of AI models, APIs, scripting, SQL/SOQL, Python, CPQ, CLM, Salesforce, and agentic platforms.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Leads architecture strategy and governance for sovereign AI solutions across sales, presales, and delivery. Designs secure AI/ML reference architectures covering data pipelines, model lifecycle management, inference infrastructure, and governance. Advises executives and customers, manages regulatory and operational risks, drives security-by-design, leads technical reviews, develops architectural documentation, and mentors architects. Requires hands-on Kubernetes, Linux, Ansible, HPC, SLURM, NVIDIA AI Enterprise, and related infrastructure experience.

Own strategic customer relationships and lead deployment of AI workflows into supplier operations. Map customer SOPs and data sources, identify high-ROI AI opportunities, translate needs into product and implementation requirements, drive adoption, partner with product/engineering, build playbooks, track value, and surface expansion opportunities across global teams.

Owns the backlog, sprint cadence, releases, and production health of enterprise AI products. Translates product strategy into requirements, user stories, work packages, and sprint goals while coordinating engineers, data scientists, business partners, and dependent teams. Leads Scrum ceremonies, PI planning, testing, release communications, and uptime monitoring. Applies AI tooling to improve delivery and uses technical fluency to prototype, review pull requests, and support responsible AI implementation.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Leads enterprise AI-native engineering initiatives by researching and prototyping AI tools, designing AI-enhanced workflows, creating reference architectures, and developing proofs of concept. Owns AI training and enablement, including documentation, presentations, tutorials, demonstrations, and coaching. Evaluates tooling, supports adoption, communicates technical changes to leadership, hosts engineering office hours, and establishes scalable standards across the organization.

Develop, train, and evaluate machine learning models for space-domain applications including object classification and anomaly detection. Build data ingestion, preprocessing, feature engineering, and model evaluation pipelines; conduct experiments; track results; and write clean, testable Python code. Collaborate with senior engineers while contributing to AI capabilities supporting space security and autonomous systems.

Leads data science and marketing analytics engagements using AI, machine learning, behavioral data, CRM, CDP, transactional, digital, and survey data. Directs modeling across segmentation, propensity, churn, customer lifetime value, and engagement analytics. Translates complex findings into executive recommendations, leads client workstreams, supports business development, mentors analysts and data scientists, and partners with research, strategy, technology, and data engineering teams.

SynX Therapeutics, a biotech start-up, invites speculative applications for future roles related to drug discovery and engineered biology.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Lead Engine’s internal AI Platform organization and technical strategy. Own the LLM gateway, agent frameworks, evaluation systems, developer-experience tooling, automation APIs, RAG and agent data infrastructure, observability, security, and cost controls. Build the initial platform hands-on, establish a unified enterprise AI stack, partner with Applied AI and Workforce Transformation leaders, and scale the organization through senior engineering leadership. Drive the Velocity program toward increased delivery autonomy and adoption across engineering.

Design, develop, deploy, and support AI solutions including generative AI applications, agents, workflow automation, document intelligence, and retrieval-augmented generation. Partner with business and IT teams to identify use cases, integrate enterprise platforms and data sources, establish prompt and evaluation standards, support AI governance and security, monitor solution performance, and train employees on responsible AI adoption.

Design, build, and maintain scalable full-stack applications; collaborate with cross-functional teams; integrate AI and agentic systems to enhance product features; ensure performance, fix bugs, and resolve bottlenecks.

Lead client-facing AI program governance and delivery as PMO manager. Translate business objectives into AI roadmaps and OKRs, oversee AI/ML lifecycle, ensure data readiness, model validation, deployment, and responsible AI compliance. Manage PMO standards, portfolio prioritization, AI-augmented reporting, and lead executive communications while mentoring PMs, TPMs, and analysts.

Lead go‑to‑market and close enterprise deals for AI/GenAI healthcare solutions across payers, providers, PBMs, and life sciences. Own revenue targets, shape solutions with architects and product teams, run POCs and demos, craft proposals and pricing, and present ROI/value to CXO stakeholders.

Lead client engagements in insurance data, analytics, and AI: define roadmaps, run discovery sessions, design solutions, manage delivery and teams, develop proposals, and ensure DataOps, cloud (AWS), governance, and best practices across AI and analytics initiatives.

Build and operate AI-driven customer acquisition infrastructure, including outbound workflows, enrichment pipelines, lead routing, personalization, audience scoring, campaign automation, experimentation, and attribution dashboards. Partner with demand generation, lifecycle marketing, data analytics, product marketing, marketing operations, and sales to translate growth objectives into scalable automated systems.

Own the vision, roadmap, and delivery of AI use-case products, including LLM systems, agents, and evaluation pipelines. Translate customer and business needs into technical product requirements, make build or buy decisions, define AI quality and business metrics, and guide launches through governance and regulatory review. Lead cross-functional teams, manage executive stakeholders, establish AI product standards, mentor product managers, and ensure product quality, safety, adoption, and impact.

Define clinical reasoning, safety boundaries, evaluation scenarios, datasets, rubrics, and acceptance thresholds for AI-enabled chronic disease products. Analyze failures and edge cases, support clinical validation and outcomes research, interpret clinical and EHR data, and collaborate with Product, Engineering, AI/ML, Data, informatics teams, and clinical stakeholders on integrations and workflows.